State-Transition-Algorithm-Based Underwater Multiple Objects Localization With Gravitational Field and Its Gradient Tensor

Abstract

Recently, several techniques using gravitational data such as gravitational field and its gradient tensor have been developed to localize underwater multiple objects. However, performances of these existed techniques largely rely on proper selections of the initial values and, thus, are likely trapped into local minima. To deal with this issue, a global optimization algorithm, named as the state transition algorithm (STA), is investigated to localize multiple objects with both gravitational field and gravitational gradient tensor data sets in this letter. Using a heuristic random search strategy, the proposed algorithm features good global search capability and avoids the dependence of using proper initial values. To assess the performance of the proposed method, different models which contain three and four underwater objects are tested. The experimental results demonstrate that the proposed method is promising with sound stability and strong antinoise ability for dynamic localization problem with multiple objects.